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多元线性过程时间可逆性札记AI 解读

We derive some readily verifiable necessary and sufficient conditions for a multivariate non-Gaussian linear process to be time-reversible, under two sets of conditions on the contemporaneous dependence structure of the innovations. One set of conditions concerns the case of independent-component innovations, in which case a multivariate non-Gaussian linear process is time-reversible if and only if the coefficients consist of essentially asymmetric columns with…

谱密度的收缩估计量AI 解读

We propose a shrinkage estimator for spectral densities based on a multilevel normal hierarchical model. The first level captures the sampling variability via a likelihood constructed using the asymptotic properties of the periodogram. At the second level, the spectral density is shrunk towards a parametric time series model. To avoid selecting a particular parametric model for the second level, a third level is added which induces an estimator that averages over a class of…

基于多项式系数密度的聚合AR(1)模型AI 解读

This paper develops a new kind of aggregation model. We extend the work of Linden (1999) to allow the AR coefficient to be drawn from a polynomial density function. The polynomial density incorporates a wealth of multi-modal density functions as special cases. Given the aggregate data, we provide estimation methods for the coefficients and the order of the polynomial density. A test for the functional form of the polynomial is provided. We apply the model to the consumption…

异质面板平稳性的均值组检验AI 解读

This paper proposes a panel-based mean group test for the null of stationarity against the alternative of unit roots in the presence of both heterogeneity across cross-section units and serial correlation across time periods. Using both sequential and joint asymptotic analyses the proposed test statistic is shown to be distributed as standard normal under the null for large N (number of groups) and large T (number of time periods). Monte Carlo results support the use of…

动态相关性的状态切换模型AI 解读

We propose a new model for the variance between multiple time series, the Regime Switching Dynamic Correlation. We decompose the covariances into correlations and standard deviations and the correlation matrix follow a regime switching model; it is constant within a regime but different across regimes. The transitions between the regimes are governed by a Markov chain. This model does not suffer from a curse of dimensionality and it allows analytic computation of multi-step…

金融因子、宏观信息与利率期限结构的预期理论AI 解读

In this paper we develop on the VAR framework, originally proposed by Campbell and Shiller (J. Political Econom 95 (1987) 1062) to evaluate the Expectations Theory, along three dimensions: the use of a testing method based on a real-time procedure, the measurement of the risk premium, the specification of the implicit monetary policy maker’s reaction function. We use financial factors and macroeconomic information to construct a test of the theory based on simulating…

不同计量设定下风险的期限结构AI 解读

This paper characterizes the term structure of risk measures such as value at risk (VaR) and expected shortfall under different econometric approaches including multivariate regime switching, GARCH-in-mean models with Student-t errors, two-component GARCH models and a nonparametric bootstrap. We show how to derive the risk measures for each of these models and document large variations in term structures across econometric specifications. An out-of-sample forecasting…

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跳跃对收益与已实现方差的影响：时间变换 Lévy 过程的计量分析AI 解读

In order to assess the effect of jumps on realised variance calculations, we study some of the econometric properties of time-changed Lévy processes. We show that in general realised variance is an inconsistent estimator of the time-change, however we can derive the second order properties of realised variances and use these to estimate the parameters of such models. Our analytic results give a first indication of the degrees of inconsistency of realised variance as an…

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基于超额质量泛函的局部平稳时间序列判别AI 解读

Discrimination of time series is an important practical problem with applications in various scientific fields. We propose and study a novel approach to this problem. Our approach is applicable to cases where time series in different categories have a different “shape.” Although based on the idea of feature extraction, our method is not distance-based, and as such does not require aligning the time series. Instead, features are measured for each time series, and…

非平稳时间序列模型的结构断点估计AI 解读

This article considers the problem of modeling a class of nonstationary time series using piecewise autoregressive (AR) processes. The number and locations of the piecewise AR segments, as well as the orders of the respective AR processes, are assumed unknown. The minimum description length principle is applied to compare various segmented AR fits to the data. The goal is to find the “best” combination of the number of segments, the lengths of the segments, and the orders of…

基于排序的依赖 Dirichlet 过程AI 解读

In this article we propose a new framework for Bayesian nonparametric modeling with continuous covariates. In particular, we allow the nonparametric distribution to depend on covariates through ordering the random variables building the weights in the stick-breaking representation. We focus mostly on the class of random distributions that induces a Dirichlet process at each covariate value. We derive the correlation between distributions at different covariate values and use…

不同长度时间序列的多变量模型估计AI 解读

Abstract We consider the problem of estimating parametric multivariate density models when unequal amounts of data are available on each variable. We focus in particular on the case that the unknown parameter vector may be partitioned into elements relating only to a marginal distribution and elements relating to the copula. In such a case we propose using a multi‐stage maximum likelihood estimator (MSMLE) based on all available data rather than the usual one‐stage maximum…

经济时间序列周期分解的贝叶斯模型平均AI 解读

Abstract A flexible decomposition of a time series into stochastic cycles under possible non‐stationarity is specified, providing both a useful data analysis tool and a very wide model class. A Bayes procedure using Markov Chain Monte Carlo (MCMC) is introduced with a model averaging approach which explicitly deals with the uncertainty on the appropriate number of cycles. The convergence of the MCMC method is substantially accelerated through a convenient reparametrization…

结构变化时点的条件概率评估AI 解读

Abstract We propose a strategy for assessing structural stability in time‐series frameworks when potential change dates are unknown. Existing stability tests are effective in detecting structural change, but procedures for identifying timing are imprecise, especially in assessing the stability of variance parameters. We present a likelihood‐based procedure for assigning conditional probabilities to the occurrence of structural breaks at alternative dates. The procedure is…

鞅变换的稳定极限及其在 GARCH 参数估计中的应用AI 解读

In this paper we study the asymptotic behavior of the Gaussian quasi maximum likelihood estimator of a stationary GARCH process with heavy-tailed innovations. This means that the innovations are regularly varying with index α∈(2,4). Then, in particular, the marginal distribution of the GARCH process has infinite fourth moment and standard asymptotic theory with normal limits and $\sqrt{n}$-rates breaks down. This was recently observed by Hall and Yao [Econometrica 71 (2003)…

间接观测下的最优变点估计AI 解读

We study nonparametric change-point estimation from indirect noisy observations. Focusing on the white noise convolution model, we consider two classes of functions that are smooth apart from the change-point. We establish lower bounds on the minimax risk in estimating the change-point and develop rate optimal estimation procedures. The results demonstrate that the best achievable rates of convergence are determined both by smoothness of the function away from the…

序列与非序列符号—秩统计量：渐近表示与渐近正态性AI 解读

The classical theory of rank-based inference is entirely based either on ordinary ranks, which do not allow for considering location (intercept) parameters, or on signed ranks, which require an assumption of symmetry. If the median, in the absence of a symmetry assumption, is considered as a location parameter, the maximal invariance property of ordinary ranks is lost to the ranks and the signs. This new maximal invariant thus suggests a new class of statistics, based on…

变化前分布含未知参数时的序贯变点检测AI 解读

In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution fθ and tries to minimize the detection delay for every possible post-change distribution gλ. In this paper, motivated by a number of practical examples, we first consider the reverse question by specifying a required detection delay at a given post-change distribution and trying to minimize the frequency of false alarms for…